Image Completion.
Image completion methods aim at synthesizing unobserved or missing image pixels conditioned on the observed pixels [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Image completion is an ill-posed problem with multiple possible solutions for the missing image regions. While early methods use deterministic models, [7]}, [8]}, [9]} used stochastic models to predict multiple instances of a complete image. Many used probabilistic models to ensure that the completions are generated according to their actual probability [10]}, [11]}, [12]}, [13]}, [14]}. We use Partial VAE [12]} – a probabilistic model – to predict the content of the complete image given only a few glimpses. However, unlike other approaches that infer image content in the pixel space, we predict content in the feature space.
